Hemorrhage Stroke Decision Making Model Based Deep Learning (BrainHemoAI System)

Condition(s) studied

Investigational drug(s) / intervention(s)

Large language model: Automatical diagnosis, treatment decision-making, and risk prediction after spontaneous intracerebral hemorrhage via a trained deep learning larger language model.

Study summary

Although hemorrhagic stroke also has the characteristics of high mortality and disability rates, and constitutes a major public health problem worldwide, there is a relative lack of in-depth research teams for hemorrhagic stroke in China. The current preoperative imaging evaluation of spontaneous cerebral hemorrhage is still limited to the traditional Tada formula, and there are subjective differences in diagnosis among different doctors, making it difficult to achieve homogenization in clinical decision-making. Hemorrhagic stroke is a common and frequently occurring disease in Jiangxi Province. Therefore, establishing a new diagnosis and treatment system focused on hemorrhagic stroke can not only fill the research gap in this field in China, improve the accuracy and homogeneity of hemorrhagic stroke diagnosis and treatment, but also promote related research progress to reduce the mortality and disability rates of this disease and improve the clinical prognosis of patients.

Eligibility

Primary outcome measure(s)

• Area Under Curve — 90-day and 180-day
  90-day and 180-day mRS score, survival status, functional independence (Barthel index).

Trial sites (1)

More Second Affiliated Hospital of Nanchang University trials in China